Abstract

We discuss how geometry, optimization, and machine learning are key technologies that are revolutionazing the way we think about data and the way we transform data into actionable models and decisions. Specifically, we explain how complex data (e.g., text, molecules, time series, images/video, supply chain flows) can be represented as geometrical objects and how this faciltates interpretation and extraction of useful information from data. We also discuss how extracted information can be mapped into decisions using optimization and machine learning models. We illustrate how to use these powerful math tools in innovative ways for analyzing complex datasets arising in molecular dynamics simulation, microscopy, chemical processes, and suppy chains. Specifically, we show that these tools can help link the microstructure of soft gels to their rheological properties, can help analyze complex responses of liquid crystals from video data, and can help detect faults and optimize large-scale systems.

Speaker Bio

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Moo Sun Hong is an Assistant Professor in the Department of Chemical and Biological Engineering at Seoul National University (SNU). He received a B.S. from SNU and an M.S. and Ph.D. from Massachusetts Institute of Technology (MIT). His honors include the AIChE Separations Division Graduate Student Research Award and the AIChE PD2M Award for Excellence in Integrated QbD Practice. His research focuses on advancing biomanufacturing systems through a systems engineering approach, integrating mechanistic modeling with data analytics and artificial intelligence to enable hybrid modeling, optimal design and control, and ultimately automated process construction.